docs: full README rewrite reflecting current architecture and capabilities
This commit is contained in:
2569718930@qq.com
@@ -1,6 +1,5 @@
|
||||
# 🌡️ PolyWeather: Intelligent Weather Quant Analysis Bot
|
||||
|
||||
[](https://github.com/yangyuan-zhen/PolyWeather/actions/workflows/python-app.yml)
|
||||
[](https://www.python.org/downloads/)
|
||||
[](https://opensource.org/licenses/MIT)
|
||||
[](https://deepwiki.com/yangyuan-zhen/PolyWeather)
|
||||
@@ -25,10 +24,11 @@ PolyWeather is a weather analysis tool built for prediction markets like **Polym
|
||||
|
||||
### 1. 🌐 Interactive Web Map Dashboard
|
||||
|
||||
- **Global Overview**: Real-time Leaflet-based map pinpointed exactly to the official Polymarket settlement airport coordinates.
|
||||
- **Progressive Background Loading**: Intelligently fetches multi-source data across all cities seamlessly without hitting rate limits.
|
||||
- **Rich Visualization**: Includes Chart.js-powered temperature trends, multi-model comparison bars, Gaussian probability distribution, and dynamic risk badges.
|
||||
- **Dual-Engine Co-existence**: Runs concurrently with the Telegram bot using a FastAPI backend, sharing the exact same data and caching layers.
|
||||
- **Global Overview**: Real-time Leaflet-based dark-themed map pinpointed to official Polymarket settlement airport coordinates.
|
||||
- **Progressive Background Loading**: Intelligently fetches multi-source data across all cities without hitting API rate limits.
|
||||
- **Rich Visualization**: Chart.js-powered temperature trends with METAR scatter overlay, multi-model comparison bars, Gaussian probability distribution, and dynamic risk badges.
|
||||
- **Cinematic Interaction**: City selection triggers a smooth fly-to zoom animation on the map.
|
||||
- **Dual-Engine Architecture**: Runs concurrently with the Telegram bot via a FastAPI backend, sharing the same data collection, analysis logic (`analyze_weather_trend`), and AI prompt pipeline.
|
||||
|
||||
### 2. 🧬 Dynamic Ensemble Blending (DEB Algorithm)
|
||||
|
||||
@@ -40,17 +40,18 @@ The system automatically tracks the historical performance of weather models (EC
|
||||
- **Accuracy Tracking**: Use the `/deb` command to view DEB's historical WU settlement hit rate and MAE, compared against individual models.
|
||||
- **Auto-Cleanup**: Only retains the last 14 days of records to prevent unbounded data growth.
|
||||
|
||||
### 2. 🎲 Math Probability Engine (Settlement Probability)
|
||||
### 3. 🎲 Math Probability Engine (Settlement Probability)
|
||||
|
||||
Automatically computes the probability for each possible WU settlement integer using a Gaussian distribution:
|
||||
Automatically computes the probability for each possible settlement integer using a Gaussian distribution:
|
||||
|
||||
- **Distribution Center μ**: Weighted average of DEB/multi-model median (70%) and ensemble median (30%). Auto-corrects upward when METAR max exceeds μ.
|
||||
- **Reality-Anchored μ**: When actual max temperature is significantly below forecasts during/after the peak window (forecast bust), μ anchors on the observed max instead of failed predictions. Otherwise, uses a weighted average of DEB/multi-model median (70%) and ensemble median (30%).
|
||||
- **Standard Deviation σ — Three-Layer Pipeline**:
|
||||
1. **Ensemble Base**: σ = (P90-P10) / 2.56
|
||||
2. **MAE Floor**: Uses DEB’s historical MAE as σ minimum—prevents ensembles from underestimating true uncertainty
|
||||
2. **MAE Floor**: Uses DEB's historical MAE as σ minimum—prevents ensembles from underestimating true uncertainty
|
||||
3. **Shock Score Amplifier**: σ × (1 + 0.5 × shock_score) when weather is changing rapidly
|
||||
- **Time Decay**: Before peak σ×1.0 → During peak σ×0.7 → After peak σ×0.3
|
||||
- **Observed Floor**: Temperatures below the current METAR max WU value are excluded
|
||||
- **Dead Market Override**: When a dead market is confirmed, probability collapses to 100% at the settled value
|
||||
|
||||
#### 💥 Shock Score: Weather Disruption Soft Scorer (0~1)
|
||||
|
||||
@@ -62,25 +63,27 @@ Evaluates environmental stability from the last 4 METAR observations. Higher = m
|
||||
| Cloud Cover Jump | 0~0.35 | Cloud code escalation (FEW→BKN, etc.) |
|
||||
| Pressure Change | 0~0.25 | >2hPa change within 2 hours |
|
||||
|
||||
### 3. 🤖 AI Deep Analysis (Groq LLaMA 3.3 70B)
|
||||
### 4. 🤖 AI Deep Analysis (Groq LLaMA 3.3 70B)
|
||||
|
||||
Feeds all weather data into LLaMA 70B, analyzed via a **P1→P4 Priority Chain**:
|
||||
Feeds all weather data into LLaMA 70B, analyzed via a **P0→P4 Priority Chain**:
|
||||
|
||||
- **P1 Real-Time Rhythm** (highest priority): 2 consecutive METAR highs → still warming; 2 non-highs past peak → dead market. Warming under low radiation → advection-driven, forecasts often underestimate.
|
||||
- **P2 Inhibitors**: Humidity >80% **and** BKN/OVC sustained 2 reports → effective suppression. "Partly cloudy" alone is insufficient.
|
||||
- **P3 Math Probability**: References settlement probability but cannot override P1 observations.
|
||||
- **P4 Forecast Background**: DEB/forecasts used for ceiling estimation; downweighted when actuals exceed them.
|
||||
- **Dead Market Trigger**: Past peak window + 2 consecutive non-highs + cloud buildup or precipitation → dead market declared.
|
||||
- **High Availability**: Auto-retry + fallback model degradation (70B → 8B) to withstand Groq API outages.
|
||||
- **P0 Forecast Bust Detection** (highest priority): Graded severity (light/medium/heavy) when actual temps diverge from forecasts. Requires slope + wind/cloud verification before declaring settlement locked. "Bust ≠ locked" — still checks for second-wave warming.
|
||||
- **P1 Real-Time Rhythm**: 2 consecutive METAR highs → still warming; 2 non-highs with slope ≤ 0 → dead market. Low-radiation warming → multi-factor (advection/mixing layer/heat island), no single-factor attribution.
|
||||
- **P2 Inhibitors** (city-aware): Precipitation → strong suppression. High humidity + thick clouds sustained 2+ reports → possible suppression, but thresholds vary by city type (maritime vs. continental). Single factor insufficient.
|
||||
- **P3 Probability Cross-Check**: References settlement probability for consistency check with P1. Contradictions explained with deviation rationale.
|
||||
- **P4 Forecast Background**: DEB/forecasts for ceiling estimation; silenced when actuals significantly deviate.
|
||||
- **Single Source of Truth**: Both web and Telegram bot share the same `analyze_weather_trend` function and `get_ai_analysis` prompt — identical context, identical decisions.
|
||||
- **High Availability**: Auto-retry + fallback model degradation (70B → 8B). Proxy support for restricted networks.
|
||||
|
||||
### 4. ⏱️ Real-time Airport Observations (Zero-Cache METAR)
|
||||
### 5. ⏱️ Real-time Airport Observations (Zero-Cache METAR)
|
||||
|
||||
- **Precise Timing**: Extracts actual observation time from raw METAR text (`rawOb`), not the API's rounded `reportTime`. Accurate to the minute.
|
||||
- **Live Passthrough**: Bypasses CDN caching via dynamic headers to obtain first-hand METAR reports.
|
||||
- **Settlement Warning**: Automatically calculates the Wunderground settlement boundary (X.5 rounding line).
|
||||
- **Anomaly Filtering**: Automatically filters out -9999 sentinel values from sources like MGM to prevent garbage data in output.
|
||||
- **Settlement Warning**: Automatically calculates the settlement boundary (X.5 rounding line).
|
||||
- **MGM Fallback**: For Turkish cities (Ankara), falls back to MGM data when METAR is unavailable.
|
||||
- **Anomaly Filtering**: Automatically filters out -9999 sentinel values to prevent garbage data in output.
|
||||
|
||||
### 5. 📈 Historical Data Collection
|
||||
### 6. 📈 Historical Data Collection
|
||||
|
||||
- Includes `fetch_history.py` to retrieve up to 3 years of hourly historical weather data (temperature, humidity, radiation, pressure, 10+ dimensions), providing data foundation for future ML models (XGBoost/MOS).
|
||||
|
||||
@@ -148,48 +151,50 @@ _(Note: The `update.sh` script automatically fetches the latest code, kills old
|
||||
|
||||
```mermaid
|
||||
graph TD
|
||||
User[User] -->|Query Command| Bot[bot_listener.py Core Scheduler]
|
||||
User[User] -->|Query| Bot["bot_listener.py (Core Scheduler)"]
|
||||
User -->|Browser| Web["web/app.py (FastAPI)"]
|
||||
|
||||
subgraph Data Acquisition
|
||||
Bot --> Collector[WeatherDataCollector]
|
||||
Web --> Collector
|
||||
Collector --> OM[Open-Meteo Forecast/Ensemble]
|
||||
Collector --> MM[Multi-Model ECMWF/GFS/ICON/GEM/JMA]
|
||||
Collector --> METAR["Live Airport METAR (rawOb precise time)"]
|
||||
Collector --> METAR["Live Airport METAR (rawOb)"]
|
||||
Collector --> MGM["MGM Fallback (Turkey)"]
|
||||
end
|
||||
|
||||
subgraph Algorithm Layer
|
||||
Collector --> Peak[Peak Hour Prediction]
|
||||
Collector --> DEB[DEB Dynamic Weighting]
|
||||
DEB --> DB[(daily_records Database)]
|
||||
Peak --> Prob[Gaussian Probability Engine]
|
||||
Peak --> Prob["Probability Engine (Reality-Anchored μ)"]
|
||||
Collector --> Prob
|
||||
METAR --> Shock[Shock Score]
|
||||
Shock --> Prob
|
||||
Collector --> Logic[Settlement Boundary / Trend Analysis]
|
||||
Collector --> Logic["Settlement Boundary / Dead Market"]
|
||||
end
|
||||
|
||||
subgraph AI Decision Layer
|
||||
DEB --> AI[Groq LLaMA 70B]
|
||||
Prob --> AI
|
||||
Logic --> AI
|
||||
METAR --> AI
|
||||
subgraph Shared Analysis
|
||||
Bot --> ATF["analyze_weather_trend()"]
|
||||
Web --> ATF
|
||||
ATF --> AI["Groq LLaMA 70B (P0→P4)"]
|
||||
end
|
||||
|
||||
AI -->|Market Call + Logic + Confidence| Bot
|
||||
Bot -->|DEB Blend + Probability + AI Analysis| User
|
||||
AI -->|Market Call + Logic + Confidence| Web
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 💡 Trading Tips
|
||||
|
||||
1. **Real-time Rhythm First**: AI analysis follows P1→P4 priority. If live METAR trends (P1) conflict with math probabilities (P3)—e.g., probability favors 7°C but it’s still surging toward 8°C—always prioritize the live trend.
|
||||
1. **Real-time Rhythm First**: AI analysis follows P0→P4 priority. If live METAR trends (P1) conflict with math probabilities (P3), always prioritize the live trend.
|
||||
2. **Watch Settlement Probabilities**: Based on Gaussian models, direction is most certain when a temperature has > 70% probability while P1 rhythm is flat.
|
||||
3. **Reference DEB Bias**: Use `/deb` to check for systematic bias. If a city is consistently "underestimated," habitually bid one WU notch higher.
|
||||
4. **Identify Dead Market Signals**: When AI declares a "Dead Market," it usually means warming power is exhausted (post-peak window + no new highs + cloud buildup). This is an opportunity to harvest remaining value.
|
||||
5. **Mind the Boundaries**: When the observed high is near X.5 (e.g., 7.50°C), be wary of Wunderground rounding up to 8 due to tiny fluctuations.
|
||||
6. **Center Point μ**: The μ value represents the expected actual high. When market prices deviate significantly from μ, an arbitrage opportunity may exist.
|
||||
4. **Identify Dead Market Signals**: When the system declares a "Dead Market," probability collapses to 100% at the settled value. Warming power is exhausted.
|
||||
5. **Mind the Boundaries**: When the observed high is near X.5 (e.g., 7.50°C), be wary of rounding up due to tiny fluctuations.
|
||||
6. **Forecast Bust Awareness**: When the AI reports a forecast bust (especially medium/heavy grade), all model predictions have lost reference value. Focus exclusively on METAR actuals.
|
||||
|
||||
---
|
||||
|
||||
_Updated 2026-03-03_
|
||||
_Updated 2026-03-04_
|
||||
|
||||
Reference in New Issue
Block a user